The architecture of Healthcare Data Warehouse Solutions
ReapMind develops enterprise data warehouses, which serve as a key component of a healthcare BI solution that includes the following components:
Data Source Layer
Data source layer contains healthcare data from internal and external data sources (ERP, EHR/EMR, CRM, claims management systems, pharmacy management systems, and so on).
Intermediate temporary storage where healthcare data is extracted, transformed, and loaded (ETL) or extracted, loaded, and transform (ELT).
Data storage layer
consists of centralized structured storage. It may also include data marts, which are subsets of healthcare DWH oriented to a specific business line (HR, accounting, etc.) or department (radiology, intensive care, pediatrics, etc.).
Analytics and BI
are tools for business analytics, data mining, data reporting, and visualization.
- Taking in structured, semi-structured, and unstructured medical data (from EHR systems, ERP, HR management systems, public medical databases, claims management systems, etc.).
- Healthcare data integration using ETL/ELT.
- Extraction and loading of complete and incremental healthcare data.
- Loading and management of healthcare data.
- Various levels of complexity in healthcare data transformation (data type conversion, summarization, etc.).
- SQL data loading and querying for healthcare.
- Big data ingestion
- Ingestion of streaming data.
- Healthcare data storage that is integrated, historical, summarised, and subject-oriented.
- PHI (Protected Health Information) storage.
- Storage of Protected Health Information (PHI).
- Metadata storage
- Healthcare data storage environment alternatives (cloud, on-premises, hybrid).
Database efficiency and reliability
- Elastic scaling of computing and storage resources
- High-performance query processing due to healthcare data indexing, materialized view support, result caching, and ML capabilities to dynamically manage performance and concurrency.
- Automated data backup across various cloud zones and regions for fault tolerance and disaster recovery.
Security and Compliance
- Granular row and column level security control.
- multi-factor authentication
- Encryption of healthcare data in transit and at rest (including backups and network connections).
- Dynamic healthcare data masking.
- Continuous assessment of vulnerabilities and threat.
- Adherence to healthcare regulations (HIPAA, FDA, HITECH, etc.).
Health Data Warehouse Solutions: Beneficial Integrations
While an enterprise DWH for healthcare can store highly structured data, semi-structured and unstructured data (manual patient records, image-based test reports, practitioner’s notes, etc.) can be stored more affordably in a data lake.
The data that is stored in the data lake is used to create the machine learning models, such as forecasting healthcare demand.
Business Intelligence Applications
Healthcare organizations can use a self-service BI system to visualize, analyze, and report the medical data arranged in the EDW in a flexible and independent manner. This makes it possible for key decision-makers to quickly and simply receive analytics insights.
Scalability and flexibility of healthcare DWH
In order to effectively address new data analytics goals, it is possible to instantly upload any kind (structured, semi-structured, unstructured) and volume of healthcare-related data.
Healthcare data protection and security measures
Following are some best practices that ReapMind highlights:
- Sensitive patient data should be processed and stored in highly secure environments (AWS, Microsoft Azure, Google Cloud or private servers).
- Make sure to use dynamic data masking, multi-factor user authentication, and constant data encryption.
- Carry out penetration testing and vulnerability analysis for DWH in healthcare, etc.
Long-standing data quality management
ReapMind empowers conducting an extensive analysis of the data warehouse system and developing strong data governance practices to ensure the high quality of the data delivered from a variety of data sources. Various encoding formats, attribute measurements from various data source systems, conflicting key fields, etc. are examples of common data quality challenges that this will aid in addressing.